In one of Professor Makridakis' competitions at MIT University (the M5 competition), machine learning techniques produced the best forecasting results using a specific dataset. Machine learning employs numerous iterations and requires termination at a certain point to achieve high accuracy. This approach utilizes labeled data to predict outcomes, where data scientists refer to "features" and "labels." Features are the explanatory variables in a regression model, while labels are the dependent variables. Among the various machine learning techniques, extreme gradient boosting regression trees are commonly used. This success inspired the integration of machine learning into existing software as an additional technique. It is crucial for users to comprehend the processes involved to interpret the results effectively. My operations research professor emphasized that if users do not understand a technique, they are unlikely to use it or might misuse it. He illustrated this point by asking his students if they would employ "dynamic programming" in practice. Most responded negatively, as they felt unable to explain the technique and its constraints comprehensively. Data scientists dedicate about 60% of their time to cleaning and organizing data, a process known as feature engineering. The challenge lies in minimizing this effort. However, is machine learning the definitive solution to real-world challenges in the Sales and Operations Planning (S&OP) domain? Establishing a sustainable S&OP process often takes several years. The primary challenges are twofold: collaboration among departments with varying mentalities, objectives, and goals, and the data required or available for use. After processing the data and employing the best technique, which may be machine learning, the outcome serves as the baseline forecast and demand. This forecast should be highly accurate. The S&OP team will then review and adjust this baseline forecast based on their collective knowledge. The M5 competition demonstrated the superiority of machine learning models over traditional statistical models. Notably, these models successfully predicted hierarchical unit sales data for Walmart, outperforming simple benchmark models like exponential smoothing by up to 20%. A key factor in this success was the use of hybrid models, combining machine learning with statistical techniques. Additionally, cross-learning methodologies, which leverage learning from multiple data series, proved particularly effective. External adjustments from broader data applied to smaller scales also enhanced accuracy. These advancements illustrate the trends in forecasting methodologies as more complex data and new predictive modeling techniques are introduced. Overall, the findings from the M5 competition underscore the importance of the correct model choice in retail forecasting, and impressively demonstrate the competitive edge a player at the winning side of the spectrum can obtain. Artificial Intelligence can sometimes produce outcomes that are difficult to explain, but its integration into forecasting processes offers significant potential for improving accuracy and efficiency.